论文标题
基于混合分布分析的模糊模型识别，用于使用小型训练数据集的轴承保持有用的寿命估算
Fuzzy model identification based on mixture distribution analysis for bearings remaining useful life estimation using small training data set
论文作者
论文摘要
本文介绍的研究工作提出了一种基于高海高吉诺（Takagi-Sugeno）（T-S）模糊推理系统（FIS）的数据驱动建模方法。此方法允许识别经典T-S FIS的参数，从少量数据开始。在这项工作中，我们在整个运行期间使用了少量轴承中的振动信号数据。 FIS模型输入是从训练轴承上定期观察到的振动信号数据中提取的特征。使用减法聚类方法确定了FIS模型每个规则的规则数量和输入参数。此外，我们建议使用混合分布分析的最大似然方法来计算时间轴上簇的参数以及与降解阶段规则相对应的概率。基于此结果，我们使用加权最小平方估计确定了每个规则的输出参数。然后，我们通过在可用数据集上进行的数值实验来通过文献中的一些现有方法对提出的方法进行了基准测试，以突出其有效性。
The research work presented in this paper proposes a data-driven modeling method for bearings remaining useful life estimation based on Takagi-Sugeno (T-S) fuzzy inference system (FIS). This method allows identifying the parameters of a classic T-S FIS, starting with a small quantity of data. In this work, we used the vibration signals data from a small number of bearings over an entire period of run-to-failure. The FIS model inputs are features extracted from the vibration signals data observed periodically on the training bearings. The number of rules and the input parameters of each rule of the FIS model are identified using the subtractive clustering method. Furthermore, we propose to use the maximum likelihood method of mixture distribution analysis to calculate the parameters of clusters on the time axis and the probability corresponding to rules on degradation stages. Based on this result, we identified the output parameters of each rule using a weighted least square estimation. We then benchmarked the proposed method with some existing methods from the literature, through numerical experiments conducted on available datasets to highlight its effectiveness.
